User Action Based Adaptive Learning with Weighted Bayesian Classification for Filtering Spam Mail

Author:

Kim Hyun-Jun,Shrestha Jenu,Kim Heung-Nam,Jo Geun-Sik

Publisher

Springer Berlin Heidelberg

Reference22 articles.

1. Korea Telecom. (2004), http://www.kt.co.kr

2. Sahami, M., Dumais, S., Heckerman, D., Horvitz, E.: A Bayesian Approach to Filtering Junk E-Mail. In: Learning for Text Categorization, Proc. of the AAAI Workshop, Madison Wisconsin. AAAI Technical Report WS-98-05, pp. 55–62 (1998)

3. Thomas, G., Peter, A.F.: Weighted Bayesian Classification based on Support Vector Machine. In: Proc. of the 18th International Conference on Machine Learning, pp. 207–209 (2001)

4. Sakkis, G., Androutsopoulos, I., Paliouras, G., Karkaletsis, V., Spyropoulos, C.D., Stamatopoulos, P.: A Memory-Based Approach to Anti-Spam Filtering for Mailing Lists. Information Retrieval 6(1), 49–73 (2000)

5. Androutsopoulos, I., Koutsias, J., Paliouras, G., Karkaletsis, V., Sakkis, G., Spyropoulos, C., Stamatopoulos, P.: Learning to Filter Spam E-mail: A Comparison of a Naïve Bayesian and a Memory-Based Approach. In: 4th PKDD Workshop on Machine Learning and Textual Information Access (2000)

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